social security programs and retirement around the world ...more recently, oshio, oishi, and...

This PDF is a selection from a published volume from the National Bureau of Economic Research

Volume Title: Social Security Programs and Retirement Around the World: Disability Insurance Programs and Retirement

Volume Author/Editor: David A. Wise, editor

Volume Publisher: University of Chicago Press

Volume ISBNs: 0-226-26257-X, 978-0-226-26257-4 (cloth); 978-0-226-26260-4 (e-ISBN)

Volume URL: http://www.nber.org/books/wise14-1

Conference Date: September 26–28, 2013

Publication Date: January 2016

Chapter Title: Option Value of Work, Health Status, and Retirement Decisions in Japan: Evidence from the Japanese Study on Aging and Retirement (JSTAR)

Chapter Author(s): Satoshi Shimizutani, Takashi Oshio, Mayu Fujii

Chapter URL: http://www.nber.org/chapters/c13330

Chapter pages in book: (p. 497 – 535)

497

12Option Value of Work, Health Status, and Retirement Decisions in JapanEvidence from the Japanese Study on Aging and Retirement (JSTAR)

Satoshi Shimizutani, Takashi Oshio, and Mayu Fujii

12.1 Introduction

This study examines retirement decisions and their associations with health status in Japan, using the option value (OV) model proposed by Stock and Wise (1990a, 1990b). This model focuses on an individual’s option value of continuing to work; that is, a utility gain achieved by keeping the option to retire in the future.

To our knowledge, only a few studies use the OV model to empirically examine retirement decisions in Japan. These studies include Oshio and Oishi (2004), which used cross- sectional data with limited information on an individual’s background. Oshio, Shimizutani, and Oishi (2010) employed macrolevel data to explore the effect of OV on the labor force participation rate; they show that OV significantly correlates with labor force participa-tion among the elderly.

More recently, Oshio, Oishi, and Shimizutani (2011)— whose study is closely related to that of Oshio, Shimizutani, and Oishi (2010)— used macro level data to construct several incentive measures vis- à- vis retirement, including OV. This study shows that since 1985, the labor force participa-

Satoshi Shimizutani is a visiting research fellow at the Institute for International Policy Studies. Takashi Oshio is a professor at the Institute of Economic Research, Hitotsubashi University. Mayu Fujii is an assistant professor at the Hokkaido University of Education.

This chapter was prepared for the Madrid meeting (September 2013) of the NBER Interna-tional Social Security Project (ISS), Phase VII. The intermediate materials were presented at the Munich meeting (January 2012), Rome meeting (May 2012), and the Paris meeting (February 2013). Any remaining errors are our own. This study utilized microlevel data collected through the Japanese Study on Aging and Retirement (JSTAR), which was conducted by the Research Institute of Economy, Trade, and Industry (RIETI) and Hitotsubashi University in 2007 and 2009. For acknowledgments, sources of research support, and disclosure of the authors’ mate-rial financial relationships, if any, please see http:// www .nber .org /chapters /c13330 .ack.

498 Satoshi Shimizutani, Takashi Oshio, and Mayu Fujii

tion rate of the elderly in Japan has been significantly sensitive to both OV and social security reforms— the latter of which featured reduced benefits generosity and thus significantly encouraged the elderly to remain in the labor force longer.

The current study presents further evidence concerning retirement deci-sions in Japan within the framework of the OV model; it makes specific reference to Japan’s disability pension program and individual health sta-tus.1 It employs microlevel data collected through the Japanese Study on Aging and Retirement (JSTAR) (Ichimura, Hashimoto, and Shimizutani 2009), which is the Japanese version of the US Health and Retirement Study (HRS), the English Longitudinal Survey on Ageing (ELSA), and the Survey on Health, Ageing and Retirement in Europe (SHARE). The JSTAR study has a longitudinal design and features a rich variety of variables that touch on health status.

We calculate OV for each individual working in 2007, with the disability pension program considered a potential pathway from work to retirement; we also examine the effect of OV on retirement decisions in 2009, while con-trolling for individual health status. We find that OV negatively and signifi-cantly correlates with the probability of retirement, and that the individual’s health does not confound the correlation.

The remainder of this chapter is organized as follows. Section 12.2 describes the institutional background. Section 12.3 explains, in detail, the study’s empirical approach. Section 12.4 presents the empirical results, and section 12.5 assesses the fit of the model. Section 12.6 shows the results of the counterfactual simulation. Section 12.7 provides concluding remarks.

12.2 Background2

12.2.1 History of Japan’s Social Security and Disability Program

This section provides an overview, from a historical perspective, of Japan’s disability program and other related reforms to the social security program. We describe the “disability pension program” below, which is often referred to as a disability insurance (DI) program in other countries. The disability pension program is part of Japan’s public pension program, and all revisions to the disability program have been linked to those made to core pension programs. Among several programs that assist the disabled, the disability pension program plays the most important role in terms of income com-pensation.

1. Ichimura and Shimizutani (2012) also employed similar JSTAR data (i.e., from the first and second waves) to explore retirement behavior in Japan. They grouped a variety of variables into health, family, and socioeconomic factors and explored the effect of each factor in the first wave (2007) on the probability of retirement and hours worked in the second wave (2009).

2. This section updates the discussion of Oshio and Shimizutani (2012).

Option Value of Work, Health Status, and Retirement Decisions in Japan 499

The Japanese public pension program consists of three programs: Employ-ees’ Pension Insurance ([EPI]; Kosei Nenkin), whose pensioners are private employees; National Pension Insurance ([NPI]; Kokumin Nenkin), whose pensioners are self- employed or agriculture, forestry, or fishery cooperative employees; and Mutual Aid Insurance ([MAI]; Kyosai Nenkin), which covers employees in the public sector and in private schools. In terms of their num-bers of pensioners, each of EPI and NPI contribute to the total by slightly less than half, and MAI occupies the remaining small portion. An overview of Japan’s social security programs is provided in the appendix.

Below, we focus on the revisions made over time to Japan’s disability pen-sion program, while focusing on EPI and NPI (table 12.1). The disability pension program was included as part of the EPI program when it was launched in 1944.3 Once disabled individuals were deemed to qualify for the program— based on functional ability to perform activities of daily living, rather than on loss of earning ability— they were rated through the use of two grades. Grade 1 refers to a condition that causes a person to be unable to perform activities of daily living (e.g., severe disability affecting both hands, or complete blindness). Grade 2 refers to a condition that causes a person to face very severe limitations in performing activities of daily living (any severe disability affecting either hand). The program at that time, from the very beginning, insured persons with mental disorders via EPI. The 1954 revision introduced Grade 3 to cover more disabled persons with conditions less severe than those in Grade 2.4

3. A brief review of development of the disability pension program is provided by the Min-istry of Health, Labour, and Welfare (2009).

4. Before 1954, EPI had in the old- age pension program only a single layer of wage- proportional benefit. While EPI was reconstructed in 1954 so as to have a double- tier structure, the disability pension program had a single- tier structure until the 1985 revision.

Table 12.1 Development of disability pension programs in Japan

National Pension Insurance (self- employed, agricultural, forestry and fishery sector)

Disability Pension (with contribution)

Disability Welfare Pension (without contribution)

Employee Pension Insurance (private firm employees)

1944 Grade 1 and Grade 2 (including mental diseases)

1954 Grade 3 was added1959 Grade 1 and Grade 2 Grade 11964–65 Covered mental diseases1974 Grade 2 was added1986– Merged to Disability Basic

Pension Disability Basic Pension +

Wage- proportional benefit

Source: Oshio and Shimizutani (2012).


Following the establishment of the EPI program the disability pension program was expanded, and there have been four major revisions during its development. The first revision was accompanied by the introduction of NPI in 1961. The NPI launched the universal pension system into the Japanese public pension program, substantially expanding the coverage of the disability pension program to more groups than just employees in the private sector. Unlike EPI, NPI did not cover mental disease at the time of its introduction. The NPI had two types of disability programs: one for recipients with premium contributions (Disability Pension Program [Shogai Nenkin]) and another for those without (Disability Welfare Pension Pro-gram [Shogai Fukushi Nenkin]). Eligibility to receive disability pension ben-efits is assessed at the time of the first doctor visit to survey the extent of the condition that rendered the person disabled; thus, those who had that first doctor visit before reaching the age of twenty or before 1961 were not insured by the Disability Pension Program under NPI. Instead, they were covered by the Disability Welfare Pension Program, which was financed by the government. Eligibility for this latter program was means tested, and its benefit amount was lower than that of the former program.

The second revision, in 1974, called for expanding coverage for men-tal disease. The NPI began to insure mental disorders in 1964 and mental deficiency in 1965, though coverage for mental disability was very limited. Whereas those who paid premiums were eligible to receive disability pension benefits once they satisfied Grade 1 or 2 criteria (NPI had no Grade 3), the disability welfare program insured the disabled only if they satisfied Grade 1 criteria. In 1974, the disability welfare program also began to cover those who satisfied Grade 2 criteria.

The third revision was implemented in 1985 (effective from 1986) as part of the major revision to core public pension programs, which harmonized all the public pension programs into an integrated form. For the first time, it reduced the benefit multiplier and flat- rate benefit in the old- age pen-sion program, and sought to restrain increases in total pension benefits. Three revisions were implemented with respect to the disability pension programs.

First, a double- tier structure was introduced, wherein (a) the flat rate Disability Basic Pension” (Shogai Kiso Nenkin) benefit was in the first tier and replaced the Disability Welfare Pension funded by the government and by the premium contributions of NPI pensioners, and (b) the wage pro-portional Disability Employees’ Pension (Shogai Kosei Nenkin) program was the second tier. The NPI pensioners, either with or without premium contributions, were entitled to receive (a). Second, both the disabled with-out premium contributions and those with premium contributions were entitled to receive the same Disability Basic Pension benefit, but the amount doubled for the former group. Third, the grading of disability conditions was harmonized across all programs. Nonetheless, the Disability Basic Pen-


sion covered only Grade 1 or 2 disabled individuals. The EPI additionally covered the disabled in Grade 3, and provided “disability compensation” for a disabled pensioner with a disability less severe than Grade 3, if that disability condition were fixed.5

Fourth, and finally, the government allowed Disability Basic Pension recipients age sixty- five years or older to additionally receive EPI benefits, if they had made any EPI contributions since 2006.

12.2.2 Current Scheme

Under the current scheme, a person who visited a doctor for the first time to consult about the cause of disability when he/she was under the age of twenty or when he/she was an NPI pensioner is entitled to receive the Disability Basic Pension benefit. There is no limitation in terms of age for receiving disability pension benefits. The formulas used to calculate the benefit are as follows:

Grade 1 = basic pension benefit × 1.25 + additional benefit for dependent children

Grade 2 = basic pension benefit + additional benefit for dependent children

The amount of the basic pension benefit is JPY 786,500 per year; that of the additional child benefit is JPY 226,300 for each of the first and second children, and JPY 75,400 for each for the third and subsequent children.

In addition to the Disability Basic Pension, any person who first consulted a doctor to identify the cause of disability when he/she was an EPI pensioner is entitled to receive a wage- proportional Disability Employees’ Pension benefit or a Disability Mutual Aid Pension benefit (for the MAI recipients). The formulas used to calculate the second- tier benefit are as follows:

Grade 1 = wage- proportional benefit × 1.25 + additional benefit for a spouseGrade 2 = wage- proportional benefit + additional benefit for a spouseGrade 3 = max (wage- proportional benefit, JPY 589,900)

The amount of additional benefit for a spouse is JPY 226,300 per year.6

12.2.3 Change in the Disability Program Participation over Time

This subsection describes changes in disability program participation over time. The data source is the Annual Report of Social Security Adminis-tration (Shakai Hoken Jigyo Nenpo), which is published by Japan’s Social

5. Since EPI pensioners were required to join the NPI as part of the 1985 reform, the en-titlement to receive disability pension became contingent on NPI grading (Disability Basic Pension), even if the disabled person had been approved to receive disability pension benefits through the EPI or MAI programs. Additionally, the MAI program has a Grade 3.

6. Momose (2008) argues that the amount of benefits from Japan’s disability employee pen-sion (Grade 1 or 2) is larger than that of the United States or Sweden, while the amount from the disability basic pension (Grade 1) is much smaller in Japan, and that Grade 2 in Japan is one- half the standard benefit in the United States or Sweden.


Security Agency. This report contains data on disability pension recipients, aggregated at the national level. However, it provides only the numbers of disability pension recipients by type of pension; no information on gen-der or age is available. Figure 12.1 reports the numbers of recipients who received disability pension benefits between 1970 and 2011.7 The numbers of recipients have increased fourfold over these four decades, from 0.5 mil-lion in 1970 to 2.2 million in 2011. The proportions of NPI, EPI, and MAI pensioners have been about 80.0 percent, 17.6 percent, and 2.3 percent, respectively, in 2011.8

Figure 12.2 shows trends in the rate of disability pension receipt in com-parison to those of the employment rate, during 1970 and 2011. Because of data unavailability, we make the strong assumption that the ratio of recipi-ents age fifty to sixty- four to all total recipients in 2009— 52.5 percent and 28.6 percent for disability EPI and NPI, respectively— has been the same over time; we also roughly estimate the total number of disability pension

7. In Japan, the fiscal year starts in April and ends in March. The figures are measured as of the end of the fiscal year.

8. The number of MAI pensioners receiving the disability pension is not available; the number of MAI pensioners eligible to receive benefits is available through the Annual Report on Social Security Statistics (Shakai Hoken Tokei Nenpo), compiled by the National Institute of Popula-tion and Social Security Research. We compute the number of MAI pensioners to receive the disability pension, assuming the proportion of those who receive out of those who are eligible— both of which are available in the Annual Report of Social Security Administration— to be the same for the EPI and MAI programs.

Fig. 12.1 The number of recipients of disability pension benefits


recipients within this age group. As already suggested in figure 12.1, the dis-ability pension receipt rate shows an upward trend, with some acceleration in the mid- 1970s and early in the twenty- first century. More importantly, the movement of the disability pension recipient rate has been unrelated to that of the employment rate.

12.2.4 Disability Program Participation by Individual Characteristics: JSTAR

We use JSTAR data to explore the characteristics of individuals par-ticipating in the disability program. The JSTAR is a longitudinal survey that collects information on middle- aged and elderly individuals in Japan. The first wave of the survey was conducted in 2007, with a baseline sample of more than 4,200 individuals age fifty to seventy- five years who lived in five municipalities in eastern Japan. The respondents in the first wave were interviewed again in 2009; the response rate in the second wave among the respondents from the first wave was about 80 percent (i.e., an attrition rate of about 20 percent), with some variation across municipalities.

Although the JSTAR sample is not nationally representative and the sample size is not large enough to contain many disability pension recipients,9

9. The JSTAR project started in 2005. Its first wave was completed in five municipalities in 2007, and its second wave in seven municipalities (two new municipalities were added) in 2009. Ten municipalities (i.e., three new municipalities were added) were studied in 2011–2012. The baseline sample consists of individuals age fifty to seventy- five years. The JSTAR uses random sampling within a municipality rather than probabilistic national sampling; it places emphasis on securing a larger sample size within the same socioeconomic environment.

Fig. 12.2 DI and employment for those age fifty to sixty- four


the data set does contain a rich set of variables representing individuals’ demographic and socioeconomic characteristics, pension receipt, and health status, inter alia; hence, we are able to examine the probability of receiving disability pension by exploiting individual- level characteristics. In the first wave of JSTAR, about 1.3 percent of the sample (age fifty to seventy- five) answered that they were receiving a disability pension at the time of the interview; in the second wave that number was 1.2 percent (age fifty- two to seventy- eight). When restricted to the sample ages fifty to sixty- four, these percentages were 1.5 percent in 2007 and 1.6 percent in 2009— both of which are smaller than the percentages estimated from the aggregated data (fig-ure 12.2).

Figures 12.3A and 12.3B illustrate the percentages of men and women age fifty- five to sixty- four, respectively, who are receiving a disability pension, by education level and year (i.e., 2007 and 2009). For both men and women and for both 2007 and 2009, we saw the general tendency that a more highly educated person is less likely to receive a disability pension. For men, how-ever, the percentage receiving a disability pension in 2007 was much higher among those who had graduated from a two- year college/vocational school than that among high school graduates; this result may derive simply from the small sample size of men who had graduated from two- year college/vocational schools (60 of 864).

Figures 12.3C and 12.3D depict the percentages of men and women age fifty- five to sixty- four, respectively, who are receiving a disability pension, by health status and year (see subsection 12.3.3 on how the health index is computed). For both men and women, the probability of receiving DI ben-efits is highest among those in the lowest health quintile (those in the poorest health), while the probability is very small among the other health quintiles. This is not surprising, given that the eligibility requirement for a disability pension in Japan places emphasis on physical and mental conditions and functional limitations (Momose 2008).

In table 12.2, we present the percentage of disability pension receipt by education and health status to highlight those subpopulations in which dis-ability pension receipt is particularly prevalent. As a general tendency, for both men and women, most recipients are concentrated within the lowest health quintile within each educational group. For example, among men the probability of disability pension receipt in 2007 of those who did not graduate from high school was 17 percent in the lowest health quintile, while it was 0–1.96 percent in the higher health quintiles. Within each health quin-tile, the percentage of disability pension receipt is lowest among those in the highest educational group. For instance, among men the probability of disability pension receipt in 2007 of those in the lowest health quintile was 17 percent in the lowest educational group, while it was 10 percent in the highest educational group.

Fig. 12.3A Probability of men age fifty- five to sixty- four in JSTAR receiving dis-ability pension by education and yearNote: The total number of observations in 2007 and 2009 were 864 and 591, respectively. In 2007/2009, the sample sizes of those whose final educational attainment is less than high school were 218/137; high school, 399/263; two- year college/vocational school, 60/51; and four- year college, 187/140.

Fig. 12.3B Probability of women age fifty- five to sixty- four in JSTAR receiving disability pension by education and yearNote: The total number of observations in 2007 and 2009 were 799 and 514, respectively. In 2007/2009, the sample sizes of those whose final educational attainment is less than high school were 202/96; high school, 409/274; two- year college/vocational school, 150/113; and four- year college, 38/31.

Fig. 12.3C Probability of men age fifty- five to sixty- four in JSTAR receiving dis-ability pension by health quintile and yearNote: The total number of observations in 2007 and 2009 were 864 and 591, respectively. In 2007/2009, the sample sizes of those in the lowest health quintile were 93/62; the second health quintile, 181/117; the third health quintile, 206/125; the fourth health quintile, 154/116; and the fifth health quintile, 230/171.

Fig. 12.3D Probability of women age fifty- five to sixty- four in JSTAR receiving disability pension by health quintile and yearNote: The total number of observations in 2007 and 2009 were 799 and 514, respectively. The sample sizes of those in the lowest health quintile were 119/58; the second health quintile, 142/84; the third health quintile, 146/91; the fourth health quintile, 223/155; and the fifth health quintile, 169/126.


12.3 Empirical Approach

To examine the differences in elderly individuals’ labor force participation across countries by the provisions of social security programs, we estimate an “inclusive” OV model, where it is assumed that an individual’s retirement decision at age t depends on an incentive measure OV, his or her own health status, and other demographic and economic characteristics at age t. This section describes the data and sample used in the estimation, the empirical model used to estimate, and how we calculate individuals’ OV figures and health status.

12.3.1 Data Source and Sample

The data we use to estimate the OV model come from the first and second waves of the JSTAR, conducted in 2007 and 2009, respectively. We restrict our sample to those who were interviewed in both waves, had answered in 2007 that they were working at all, and had provided information on their

Table 12.2 Percentage of disability pension recipients: JSTAR men and women ages fifty- five to sixty- four by health quintile and education, 2007 and 2009

Percent receiving Health quintile

Education 1 2 3 4 5

Year 2007Men

Less than high school 17.24 0 0 0 1.96High school 12.50 0 0 0 0Two years college/vocational school 25.00 5.26 0 0 0Four years college or more 10.00 0 0 0 0

WomenLess than high school 9.09 2.04 0 0 0High school 0 0 1.19 1.53 0Two years college/vocational school 0 0 0 0 0Four years college or more 0 0 0 0 0

Year 2009Men

Less than high school 11.11 0 0 6.90 0High school 14.81 0 0 2.08 0Two years college/vocational school 0 0 6.67 0 0Four years college or more 7.14 0 0 0 0

WomenLess than high school 16.67 0 0 0 0High school 2.94 4.26 0 0 1.59Two years college/vocational school 25.00 0 0 0 0Four years college or more 0 0 0 0 0

Note: For 2007, the total numbers of observations are 864 men and 799 women. For 2009, the total numbers of observations are 591 men and 514 women.


demographic, socioeconomic, and health- related characteristics in both waves. Individuals who answered in 2007 that they were working at all con-sisted of company executives, regular employees, part- time workers, casual or temporary workers, self- employed workers, employees on leave, and oth-ers. Our final sample consists of 1,575 individuals (996 men, 579 women) who were age fifty to seventy- five in 2007.

12.3.2 Empirical Model

We specify the empirical OV model as follows:

(1) P Y Health X

Health X

i i i i

i i i i

=

= ε > − − − −� � � � �

( 1 | OV , , )

( OV ),

,2009 ,2007 ,2007 ,2007

0 1 ,2007 2 ,2007 3 ,2007

The dependent variable is an indicator that takes a value of 1 if individual i retired in 2009, and zero otherwise. We define an individual as “retired” if he/she answered that he/she was retired or doing housework. One of the main independent variables, OVi ,2007, is the OV of individual i in 2007; Healthi,2007

is another key independent variable because it represents the health status of individual i. Finally, Xi ,2007 represents a vector of the remaining control variables, including age, gender, marital status (married), the existence of a working spouse, total assets, occupation, and educational attainment, all as measured in 2007.10 In the following sections, we describe how the OV and health status are measured.

12.3.3 OV Calculations

The OV at age t is defined as:

(2) Ptk

K

k ktOV OV1

∑==

,

where k refers to the kth pathway to retirement; Pk, the probability weight on the kth pathway, and OVkt , the OV of the kth pathway at age t. Moreover, we define the OV corresponding to each pathway as:

(3) OVkt = EtVkt(r*) – EtVkt(t),

(4) E V r p y s P B st kts t

r

s ts t

s r

D

s ts t

rk� � �� ( ) ( ( )) [ ( )]1

| |∑ ∑= +=

−−

=

− ,

where r* represents the value of retirement age that maximizes EtVkt(r); ps|t is the probability of survival at age s, given survival at age t; y(s) refers to wage income at age s while working; Brk(s) refers to pension benefits at age

10. There are some missing data for each variable; this is especially true for the total assets. We impute the variable by allocating the average value of those who have attributes similar to those of the respondents.


s when retired at age r through the kth pathway; β is the discount rate; γ is the parameter of risk aversion; κ is the parameter of labor disutility; and D is the maximum age.

In the following sections, we consider in turn the essential components of the OV model: pathways to retirement; weights on the pathways to retire-ment; the measurement of health, profiles of wage, and pension income; and assumptions regarding model parameters.

Pathways to Retirement

Based on Japan’s old- age pension system and its three related programs, we set up two pathways to retirement— namely, normal claiming and disability— for each of the EPI/MAI pensioners. More explicitly, for EPI/MAI pensioners, we assume:

A. Normal claiming: “Employed” to “normal claiming,” which entails claiming at the normal retirement age. In this pathway, one claims wage- proportional benefits at ages sixty to sixty- five (depending on cohort) and a flat- rate benefit at age sixty- five. The benefits are reduced as per the results of the earnings test under the Zaishoku pension scheme, if one continues to work beyond the normal retirement age.11

B. Disability: “Employed” to “claiming disability pension benefit,” which entails claiming EPI/MAI wage- proportional/flat- rate benefits at ages sixty to sixty- four.

For NPI pensioners, we assume:

A. Normal claiming: “Self- employed” to “normal claiming,” which entails claiming NPI benefits at age sixty- five. Note that no earnings test is applied to NPI beneficiaries.

B. Disability: “Self- employed” to “claiming NPI disability pension ben-efit,” which entails claiming disability benefits initially at ages sixty to sixty- four.

The Weight Given to Each Pathway

To compute the weight given to each retirement pathway in calculating OV, we employ a “stock estimator” that uses the share of the population tak-ing each pathway in a combined age group at a given point in time.12 In fact, since we assume there to be only two retirement pathways for each pension

11. In addition, there are “early claiming” (Kuriage) and “late claiming” (Kurisage) schemes, which actuarially adjust the benefit. Even when incorporating these claim schemes, we found the estimation results to remain virtually identical.

12. There are two alternatives to the stock estimator. One is the “age- specific flow” (i.e., the share of workers at each age who enter a pathway at that age),and the second is the “aggregated flow” (i.e., the share of workers starting at an initial age who eventually enter a pathway at any point). This latter approach reflects the actual ultimate experience of the cohort, but it does need to assume perfect foresight vis- à- vis future changes to stringency.


system (EPI/MAI or NPI), we estimate the weight of the disability pathway and assign 1— (the weight estimate on the disability pathway) to the normal claiming pathway. The stock estimator of the weight of the disability path is calculated by year, gender, and education. More specifically, we calculate the estimate of the weight of each pathway as follows:

A. EPI/MAI “Disability”: Share of EPI/MAI disability pension enrollees ages sixty to sixty- four, among all EPI/MAI enrollees, by year, gender, and education.

B. EPI/MAI “Normal claiming”: Others (1–[A]).C. NPI “Disability”: Share of NPI disability pension enrollees ages sixty

to sixty- four, among all NPI enrollees, by year, gender, and education.D. NPI “Normal claiming”: Others (1–[C]).

Administrative data from the Annual Report on Social Security Administra-tion are used to obtain information by gender; since administrative data lack information by education, data from the JSTAR are exploited to comple-ment the information in terms of education.

Figures 12.4A and 12.4B illustrate the weight on each pathway for the EPI/MAI and NPI groups, respectively, by JSTAR wave year and gender. The proportion of disability is negligible for both the male and female EPI/MAI groups, while the amount is somewhat higher for the NPI group.

Earnings and Pension Benefits

The major components of the OV calculation are the labor income until retirement and the pension income between retirement and death. Labor income until retirement is calculated by taking the following steps. First,

Fig. 12.4A Weights on pathways to retirement, EPI


we obtain the median earnings by age, gender, and pension membership (NPI or EPI/MAI), using 2007 data (figure 12.5). Second, for each gender- by- pension- membership group, we calculate (median earnings among indi-viduals age A + 1 – median earnings among individuals age A)/(median earnings among individuals age A) (A = 50, . . .,74) and assume that the rates represent how an individual’s earnings grow as one ages. Then, to obtain an

Fig. 12.4B Weights on pathways to retirement, NPI

Fig. 12.5 Monthly wage projection (in 2011 euros), ages fifty to seventy- five


individual’s wage profile from age fifty to seventy- five, we multiply the indi-vidual’s actual earnings observed in 2007 back and forward by the growth rate of median earnings. Finally, we need to estimate the earnings for indi-viduals under age fifty or over seventy- five because the JSTAR sample does not contain individuals within these age ranges. Therefore, for simplicity, we assume that the earnings of those under age fifty are the same as those of individuals at age fifty. We also assume that all individuals will stop work-ing by age seventy- five to avoid imputing an earnings profile beyond age seventy- five.

The pension benefit between retirement and death of an individual is then estimated using information on the individual’s pension membership, years of premium contributions, career average monthly wage (CAMW), and benefit multiplier. Since information on an individual’s years of premium contributions is not available through the JSTAR data, we use the years worked as a proxy. The CAMW is estimated via the earnings projection, as explained in the previous paragraph. The benefit multiplier is determined exogenously by gender and birthday. For technical reasons, spouse and sur-vivor benefits are not included.

Other Parameters

In calculating the OV, we assume several parameter values. We set the value of δ, the parameter of risk aversion, to equal 0.03; κ, the parameter of labor disutility, to equal 1.5; and γ to equal 0.75. All of these parameters are uniform across countries.13 The mortality rates are taken from the 2007 “Life Table,” published by the Ministry of Health, Labour, and Welfare.

OV Calculations and Descriptions

To examine the retirement behavior in 2009 of individuals who were work-ing in 2007, we calculate the OV of these individuals in 2007, using the information described in previous subsections. Figures 12.6A and 12.6B show the mean OV by age in 2007 for men and women, respectively. Figure 12.6A shows that, for men, the mean OV for the normal claiming path (OV- normal) constantly declines with age, but remains positive even among those at age seventy- four. The mean OV for the disability path (OV- disability) levels off in the fifties and then begins to decline from the mid- sixties, where it overlaps with the mean OV- normal. The inclusive OV overlaps with the mean OV- normal because the weight of the disability path is very small. The result— that the inclusive OV decreases with age— indicates that the utility gained from working until the optimal retirement age, compared to that from retiring at the current age, tends to decline as people age. At the same time, however, the result that the inclusive OV remains positive until

13. The likelihood function is very flat, and the greater the assumed value for the risk aversion parameter γ, the lower the estimated OV coefficient will be. Given the flat likelihood function, we believe that little is to be gained from showing estimates based on other values.


age seventy- four implies that, in our OV model, it is optimal to delay retire-ment until age seventy- four. The relationship between the OV and age is similar for women, except that the OV level is much lower than that of men (figure 12.6B).

To further examine the relationship between OV and age, figures 12.6C and 12.6D illustrate— by age in 2007 and for men and women, respectively—

Fig. 12.6A Mean OV by age for men (ages fifty to seventy- four)

Fig. 12.6B Mean OV by age for women (ages fifty to seventy- four)

Fig. 12.6C Mean PDV- normal and PDV- disability by age, men ages fifty to seventy- four (2011 euros)

Fig. 12.6D Mean PDV- normal and PDV- disability by age, women ages fifty to seventy- four (2011 euros)


the mean present discounted value (PDV) of pension benefits obtained, if retired. Figure 12.6C shows that, for men, the mean PDV of the normal claiming path (PDV- normal) rises until age sixty- five (i.e., the eligibility age for the basic public pension). Beyond age sixty- five the mean PDV- normal declines because by delaying retirement beyond age sixty- five, individuals must relinquish years of benefit receipt. In contrast, the mean PDV of the disability path (PDV- disability) declines continuously with age and beyond age sixty- five becomes the same as that of PDV- normal (i.e., the age at which the disability program is unified into the core public pension programs [EPI/MAI or NPI]). Figure 12.6D shows that a similar pattern also holds for women. These results indicate that the mean OV remains positive until age seventy- four, not because the PDV increases with age, but because the addi-tional earnings obtained through continued work more than compensate for the decline in the PDV.

12.3.4 Health Measurements

A measure of individual health status is calculated using the method of Poterba, Venti, and Wise (2010). The idea behind this approach is to con-struct a single index by using information from many health indicators. More specifically, using data pooled from the first and second JSTAR waves, we apply a principal component analysis to twenty- two health indicators and use the first principal component to construct a continuous health index.14 The health index is then converted into percentile values, with 1 representing the worst health and 100 the best health. The appendix table 12A.1 reports the loadings on each health indicator.

Figure 12.7 shows the average percentile of the health index by age, for both men and women. We observe that (a) the mean percentile of the health index declines with age, (b) the speed of the decline is slightly higher in the sixties than in the fifties, and (c) men and women share almost the same pat-tern, although the mean is slightly higher for men in the late sixties.

12.4 Results

Table 12.3A reports the results of estimating the OV model in equation (1). Columns (1)–(8) relate to different model specifications, which vary by whether the effect of age is being controlled for linearly or with dummy

14. The twenty- two items are as follows: (1) difficulty in walking 100 m, (2) difficulty in lifting/carrying, (3) difficulty in pushing/pulling, (4) difficulty with an activity of daily living, (5) difficulty in climbing a few steps, (6) difficulty in stooping/kneeling/crouching, (7) difficulty in getting up from a chair, (8) self- reported health fair/poor, (9) difficulty in reaching/extending an arm up, (10) body mass index, (11) difficulty in sitting for two hours, (12) difficulty in picking up a dime, (13) ever experienced heart problems, (14) hospital stay, (15) doctor visit, (16) ever experienced psychological problems, (17) ever experienced a stroke, (18) ever experienced high blood pressure, (19) ever experienced lung disease, (20) ever experienced diabetes, (21) ever experienced arthritis, and (22) ever experienced cancer.


variables, whether the effect of health is being controlled for using quintile health dummies or a continuous health index, and whether covariates other than OV and health are included. Column (1), which controls only for a con-tinuous age variable and health quintile dummies, shows that the coefficient on OV- inclusive is negative and statistically significant. The result indicates that OV- inclusive has a negative effect on retirement. More specifically, a 10,000- unit increase in OV decreases the probability of retirement by 2.1 percentage points; a standard deviation increase in the OV (shown in the brackets), meanwhile, decreases the probability of retirement by 4.8 percent-age points. These results also hold for other specifications (columns [2]–[8]). The coefficients on the other variables generally have the expected signs. Columns (1)–(4) show that, compared to individuals in the lowest health quintile (i.e., those in the poorest health), those in the higher health quintiles are less likely to retire, although the health effects are neither monotonic nor statistically significant. Similarly, the results in columns (5)–(8) indicate that having a larger continuous health index value (i.e., being healthier) makes one less likely to retire, although again the coefficient is not significantly different from zero.

To confirm that the above results are robust to the scale of the OV mea-sure, we also estimate the model using a percent gain in the inclusive OV as a key explanatory variable, instead of its level. Here, the measure of a percent gain in the inclusive OV is calculated by dividing the OV (i.e., the difference between the peak level and the current level of utility) by the current level of

Fig. 12.7 Mean percentile of health index by age and gender (ages fifty to seventy-eight)

Tab

le 1

2.3A

E

ffec

t of

incl

usiv

e O

V o

n re

tire

men

t

Spec

ifica

tion

(1)

(2

)

(3)

(4

)

(5)

(6

)

(7)

(8

)

OV

_inc

lusi

ve–0

.021

3**

–0.0

224*

*–0

.020

4**

–0.0

219*

*–0

.021

6**

–0.0

226*

*–0

.020

7**

–0.0

221*

*

(0.0

056)

(0.0

056)

(0.0

061)

(0.0

061)

(0.0

056)

(0.0

056)

(0.0

061)

(0.0

061)

[–

0.04

8][–

0.05

1][–

0.04

7][–

0.05

2][–

0.04

9][–

0.05

2][–

0.04

7][–

0.05

2]H

ealt

h qu

int 2

–0.0

159

–0.0

154

–0.0

179

–0.0

166

(sec

ond

low

est)

(0.0

184)

(0.0

180)

(0.0

173)

(0.0

169)

Hea

lth

quin

t 30.

0064

0.00

68–0

.000

40.

0003

(0

.020

9)(0

.020

5)(0

.019

1)(0

.018

7)

H

ealt

h qu

int 4

–0.0

077

–0.0

086

–0.0

12–0

.012

(0

.020

6)(0

.020

0)(0

.019

2)(0

.018

6)

H

ealt

h qu

int 5

–0.0

047

–0.0

054

–0.0

069

–0.0

073

(hig

hest

)(0

.020

5)(0

.019

8)(0

.019

3)(0

.018

6)

H

ealt

h in

dex

–0.0

0016

2–0

.000

17–0

.000

216

–0.0

0022

(0

.000

3)(0

.000

3)(0

.000

3)(0

.000

2)A

ge0.

0035

5*

0.00

321*

0.

0033

5*

0.00

300+

(0.0

016)

(0

.001

6)

(0.0

016)

(0

.002

)

Age

dum

mie

s

Incl

uded

Incl

uded

Incl

uded

Incl

uded

Fem

ale

0.01

520.

0128

0.01

590.

0136

(0

.017

2)(0

.016

7)

(0

.017

1)(0

.016

6)M

arri

ed

0.

0267

+0.

0244

0.02

71+

0.02

47

(0.0

160)

(0.0

159)

(0.0

160)

(0.0

159)

(con

tinu

ed)

Spec

ifica

tion

(1)

(2

)

(3)

(4

)

(5)

(6

)

(7)

(8

)

Spou

se w

orks

–0.0

528*

*–0

.051

2**

–0.0

530*

*–0

.051

3**

(0

.016

1)(0

.015

7)

(0

.016

2)(0

.015

7)To

tal a

sset

s

0.

0000

30.

0000

2

0.

0000

20.

0000

1(i

n m

illio

ns o

f eu

ros)

(0.0

001)

(0.0

001)

(0.0

001)

(0.0

001)

Occ

up. d

umm

ies

Incl

uded

Incl

uded

Incl

uded

Incl

uded

Edu

c.: <

Hig

h sc

hool

–0.0

263

–0.0

257

–0.0

274

–0.0

27

(0.0

216)

(0.0

210)

(0.0

216)

(0.0

209)

Edu

c.: H

igh

scho

ol

–0

.025

8–0

.027

6

–0

.028

4–0

.030

2

(0.0

217)

(0.0

212)

(0.0

216)

(0.0

211)

Edu

c.: T

wo

yrs.

col

lege

/voc

atio

nal s

choo

l

–0.0

036

–0.0

0286

–0.0

0537

–0.0

0451

(0.0

265)

(0.0

262)

(0.0

262)

(0.0

259)

No.

of

obse

rvat

ions

1,57

51,

575

1,57

51,

575

1,57

51,

575

1,57

51,

575

Mea

n re

t. r

ate

0.09

60.

096

0.09

60.

096

0.09

60.

096

0.09

60.

096

Mea

n of

OV

32,1

8532

,185

32,1

8532

,185

32,1

8532

,185

32,1

8532

,185

Std.

dev

. of

OV

20

,581

20

,581

20

,581

20

,581

20

,581

20

,581

20

,581

20

,581

Not

es:

Coe

ffici

ents

are

mar

gina

l eff

ects

of

a 10

,000

- uni

t ch

ange

in O

V f

rom

pro

bit

mod

els.

Sta

ndar

d er

rors

are

sho

wn

in p

aren

thes

es. T

he e

ffec

t of

a o

ne

stan

dard

dev

iati

on c

hang

e in

OV

is s

how

n in

bra

cket

s (t

his

is e

stim

ated

as

the

effec

t of

incr

easi

ng in

clus

ive

OV

fro

m t

he c

urre

nt v

alue

–0.

5 st

d. d

ev t

o th

e cu

rren

t val

ue +

0.5

std.

dev

.).

***S

igni

fican

t at t

he 1

per

cent

leve

l.**

Sign

ifica

nt a

t the

5 p

erce

nt le

vel.

*Sig

nific

ant a

t the

10

perc

ent l

evel

.

Tab

le 1

2.3A

(c

onti

nued

)


utility. Table 12.3B shows the results of the estimations. The estimated coef-ficients are again negative and significant, and robust to specification choice.

To investigate whether the effects of OV vary by individual health, we estimate the OV model separately for each health quintile; the estimations results are summarized in table 12.4A. Specifications (1)–(4) in columns (1)–(4) are the same as those in columns (1)–(4) of table 12.3A. The results show that the coefficient on the inclusive OV is negative for all the health quintile groups, but statistically significant only for the second, third, and fifth quin-tile groups. Similar results hold when we use a percent gain in the inclusive OV (table 12.4B). While insignificant coefficients on the OV among some of the health quintile groups may derive from the small sample size used in the estimations (indeed, the standard error of the coefficient on the OV is larger when the model is estimated separately for each health quintile), the results in table 12.4C also indicate that the effects of OV on the retirement decision do not vary monotonically with individual health. This table reports the results of estimating a model that includes an interaction between OV and the continuous health index, instead of estimating the model separately for each health quintile. While the estimated coefficient on the interaction term is negative— which may indicate that the financial incentives for retirement matter more for those in better health— it is not statistically significant.

In table 12.5A, we present the results of OV model estimation for each education group. The results show no consistent pattern of OV effects across the various education groups: the coefficient on the OV is negative and significant only for those who graduated from high school and those

Table 12.3B Effect of percent gain in inclusive OV on retirement

Specification

(1) (2) (3) (4)

Percent gain in OV –0.0416** –0.0424** –0.0416** –0.0422** (0.0126) (0.0131) (0.0123) (0.0126)

Linear age X X Age dummies X XHealth quintiles X X X XOther Xs X X

No. of observations 1,575 1,575 1,575 1,575Mean ret. rate 0.096 0.096 0.096 0.096Mean of % gain in OV 1.380 1.380 1.380 1.380Std. dev. of % gain in OV 1.011 1.011 1.011 1.011

Notes: Models are the same as models 1–4 in table 12.1. Coefficients are marginal effects. Standard errors are shown in parentheses.***Significant at the 1 percent level.**Significant at the 5 percent level.*Significant at the 10 percent level.

Tab

le 1

2.4A

E

ffec

t of

incl

usiv

e O

V o

n re

tire

men

t by

heal

th q

uint

ile

No.

of

obs.

Mea

n re

t. r

ate

Mea

n

of O

VSt

d. d

ev.

of O

V

Spec

ifica

tion

(1)

(2

)

(3)

(4

)

OV

: Low

est q

uint

ile31

10.

1190

27,2

68.8

918

,995

.95

–0.0

166

–0.0

168

–0.0

0508

–0.0

0307

(wor

st h

ealt

h)

(0

.015

9)(0

.014

1)(0

.015

8)(0

.014

0)

[–0.

0322

][–

0.03

29]

[–0.

0104

][–

0.00

6]O

V: 2

nd q

uint

ile30

30.

0957

30,9

07.5

921

,073

.72

–0.0

270*

*–0

.032

2**

–0.0

154+

–0.0

212*

(0

.010

3)(0

.009

6)(0

.008

8)(0

.008

5)

[–0.

08]

[–0.

0969

][–

0.05

05]

[–0.

0715

]O

V: 3

rd q

uint

ile28

70.

1185

31,8

93.5

920

,732

.61

–0.0

394*

*–0

.039

3**

–0.0

488*

*–0

.044

3**

(0

.013

4)(0

.013

9)(0

.015

9)(0

.015

4)

[–0.

0887

][–

0.09

24]

[–0.

135]

[–0.

132]

OV

: 4th

qui

ntile

306

0.07

8435

,057

.42

19,7

04.5

5–0

.008

33–0

.005

23–0

.008

83–0

.007

41

(0.0

110)

(0.0

108)

(0.0

100)

(0.0

089)

[–

0.01

72]

[–0.

012]

[–0.

02]

[–0.

0188

]O

V: H

ighe

st q

uint

ile28

60.

0944

34,7

28.7

221

,178

.41

–0.0

168

–0.0

199+

–0.0

171+

–0.0

202*

(bes

t hea

lth)

(0.0

108)

(0.0

107)

(0.0

100)

(0.0

103)

[–

0.04

][–

0.05

04]

[–0.

0455

][–

0.05

31]

Lin

ear

age

X

X

Age

dum

mie

s

X

XO

ther

Xs

X

X

Not

es: M

odel

s ar

e th

e sa

me

as m

odel

s 1–

4 in

tabl

e 12

.1, b

ut a

re e

stim

ated

sep

arat

ely

by h

ealt

h qu

inti

le; e

ach

coeffi

cien

t on

the

tabl

e is

from

a d

iffer

ent r

egre

s-si

on.

Coe

ffici

ents

are

mar

gina

l eff

ects

of

a 10

,000

- uni

t ch

ange

in

OV

fro

m p

robi

t m

odel

s. S

tand

ard

erro

rs a

re s

how

n in

par

enth

eses

. T

he e

ffec

t of

a o

ne

stan

dard

dev

iati

on c

hang

e in

OV

is s

how

n in

bra

cket

s (t

his

is e

stim

ated

as

the

effec

t of

incr

easi

ng in

clus

ive

OV

fro

m t

he c

urre

nt v

alue

–0.

5 st

d. d

ev. t

o th

e cu

rren

t val

ue +

0.5

std.

dev

.).

***S

igni

fican

t at t

he 1

per

cent

leve

l.**

Sign

ifica

nt a

t the

5 p

erce

nt le

vel.

*Sig

nific

ant a

t the

10

perc

ent l

evel

.

Tab

le 1

2.4B

E

ffec

t of

perc

ent g

ain

in in

clus

ive

OV

on

reti

rem

ent b

y he

alth

qui

ntile

No.

of

obs.

Mea

n re

t. r

ate

Mea

n of

%

OV

Std.

dev

. of

% O

V

Spec

ifica

tion

(1)

(2

)

(3)

(4

)

OV

: Low

est q

uint

ile31

10.

1190

1.21

7062

0.95

8973

9–0

.035

–0.0

452

–0.0

331

–0.0

395

(wor

st h

ealt

h)

(0

.028

8)(0

.027

7)(0

.026

8)(0

.025

0)O

V: 2

nd q

uint

ile30

30.

0957

1.16

6729

0.83

9714

4–0

.060

2*–0

.085

0**

–0.0

403+

–0.0

577*

*

(0.0

289)

(0.0

256)

(0.0

222)

(0.0

217)

OV

: 3rd

qui

ntile

287

0.11

851.

4263

231.

1223

53–0

.052

5–0

.043

5–0

.055

8+–0

.046

4

(0.0

328)

(0.0

342)

(0.0

299)

(0.0

302)

OV

: 4th

qui

ntile

306

0.07

841.

5727

161.

0287

07–0

.036

2–0

.023

6–0

.031

9+–0

.022

7

(0.0

242)

(0.0

207)

(0.0

193)

(0.0

161)

OV

: Hig

hest

qui

ntile

286

0.09

441.

5456

941.

0597

86–0

.061

1**

–0.0

676*

*–0

.053

4**

–0.0

604*

*(b

est h

ealt

h)

(0

.019

0)(0

.022

5)(0

.016

9)(0

.019

8)

Lin

ear

age

X

X

Age

dum

mie

s

X

XO

ther

Xs

X

X

Not

es: M

odel

s ar

e th

e sa

me

as m

odel

s 1–

4 in

tabl

e 12

.1, b

ut a

re e

stim

ated

sep

arat

ely

by h

ealt

h qu

inti

le; e

ach

coeffi

cien

t on

the

tabl

e is

from

a d

iffer

ent r

egre

s-si

on. C

oeffi

cien

ts a

re m

argi

nal e

ffec

ts. S

tand

ard

erro

rs a

re s

how

n in

par

enth

eses

.**

*Sig

nific

ant a

t the

1 p

erce

nt le

vel.

**Si

gnifi

cant

at t

he 5

per

cent

leve

l.*S

igni

fican

t at t

he 1

0 pe

rcen

t lev

el.


who graduated from a two- year college or vocational school. Using the measure of percent gain in OV- inclusive does not essentially change this result (table 12.5B).

12.5 Model Fit

To examine the fit of the OV model, we first compare the retirement hazard rate predicted by the OV model (specified in column [4] of table 12.3A) with the actual hazard rate. Two things should be noted, however, in conducting the examination. First, since our data set is a short panel, the hazard rate is obtained not by following the same individuals over time, but by assuming that the variation in hazard rates by cohort at a given point in time is the same as that by age of a single cohort. More specifically, we cal-culate the hazard rate by taking the average by age in 2007 of the probability of retirement within a year, given that the individual is working in 2007. Second, the retirement hazard rate averaged by age can be a noisy measure because the number of observations at each age is small. As an extreme ex-ample, in 2007, only three female individuals at age seventy- four were still working.

Figures 12.8A and 12.8B compare the predicted versus actual retirement hazard rates for men and women, respectively. Figure 12.8A shows that, for men, there are some gaps between the predicted and actual retirement

Table 12.4C Effect of inclusive OV on retirement with health index interaction

Specification

(1) (2) (3) (4)

OV –0.0167 –0.019 –0.016 –0.0185 (0.0116) (0.0124) (0.0113) (0.0121)OV*health index –0.00005 –0.00003 –0.00004 –0.00002 (0.0001) (0.00005) (0.0001) (0.0000)Health index 0.00004 –0.00002 –0.00002 –0.00007 (0.00045) (0.00046) (0.00043) (0.00044)

Linear age X X Age dummies X XOther Xs X X

No of observations 1,575 1,575 1,575 1,575Mean ret. rate 0.096 0.096 0.096 0.096Mean of OV 32,185 32,185 32,185 32,185Std. dev. of OV 20,581 20,581 20,581 20,581

Notes: Models are the same as models 5–8 in table 12.1, with the addition of an OV*health index interaction. Coefficients are marginal effects of a 10,000- unit change in OV from probit models. Standard errors are shown in parentheses. The effect of a one standard deviation change in OV is shown in brackets (this is estimated as the effect of increasing inclusive OV from the current value –0.5 std. dev. to the current value +0.5 std. dev.).

Tab

le 1

2.5A

E

ffec

t of

incl

usiv

e O

V o

n re

tire

men

t by

educ

atio

n gr

oup

No.

of

obs.

Mea

n re

t. r

ate

Mea

n

of O

VSt

d. d

ev.

of O

V

Spec

ifica

tion

(1)

(2

)

(3)

(4

)

OV

: < H

igh

scho

ol42

80.

1332

22,9

5817

,059

0.00

112

–0.0

124

0.01

22–0

.005

45

(0.0

146)

(0.0

125)

(0.0

164)

(0.0

144)

[0

.002

][–

0.02

2][0

.022

][–

0.01

]O

V: H

igh

scho

ol69

00.

0899

30,4

6317

,818

–0.0

304*

*–0

.030

5**

–0.0

360*

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7**

(0

.008

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0)(0

.008

5)(0

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1)

[–0.

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7][–

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[–0.

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2)(0

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8)

[–0.

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04]

[–0.

0653

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OV

: Fou

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532

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0.01

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093)

(0.0

095)

(0.0

105)

(0.0

098)

[0

.003

58]

[0.0

158]

[0.0

2271

][0

.042

3]

Lin

ear

age

X

X

Age

dum

mie

s

X

XH

ealt

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les

XX

XX

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er X

s

X

X

Not

es: M

odel

s are

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sam

e as

mod

els 1

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tabl

e 12

.1, b

ut a

re e

stim

ated

sepa

rate

ly b

y ed

ucat

ion

grou

p; e

ach

coeffi

cien

t in

the

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e is

from

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egre

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ents

are

mar

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l eff

ects

of

a 10

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t ch

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in

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m p

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tand

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re s

how

n in

par

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. T

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t of

a o

ne

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e in

OV

is s

how

n in

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as

the

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t of

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ng in

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OV

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m t

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nt v

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–0.

5 st

d. d

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ue +

0.5

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igni

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he 1

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cent

leve

l.**

Sign

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nt a

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vel.

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Tab

le 1

2.5B

E

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t of

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ent g

ain

in in

clus

ive

OV

on

reti

rem

ent b

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ion

grou

p

Spec

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tion

No.

of

obs.

M

ean

ret.

rat

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Mea

n of

%

OV

St

d. d

ev.

of %

OV

(1

)

(2)

(3

)

(4)

OV

: < H

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scho

ol42

80.

1332

1.00

70.

762

–0.0

477

–0.0

598+

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432

–0.0

557+

(0

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7)(0

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6)(0

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1)(0

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V: H

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1.37

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0)(0

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0904

1.69

20.

815

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118

–0.0

185

–0.0

0832

–0.0

16

(0.0

337)

(0.0

334)

(0.0

256)

(0.0

247)

OV

: Fou

r yr

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50.

0821

1.81

91.

306

–0.0

110.

0010

3–0

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8–0

.000

73

(0.0

155)

(0.0

147)

(0.0

139)

(0.0

116)

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ear

age

X

X

Age

dum

mie

s

X

XO

ther

Xs

X

X

Not

es: M

odel

s ar

e th

e sa

me

as m

odel

s 1–

4 in

tabl

e 12

.1, b

ut a

re e

stim

ated

sep

arat

ely

by e

duca

tion

gro

up; e

ach

coeffi

cien

t on

the

tabl

e is

from

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iffer

ent r

e-gr

essi

on. C

oeffi

cien

ts a

re m

argi

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ffec

ts. S

tand

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re s

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hazard rates; in particular, the actual retirement hazard rate is much higher at age sixty- five and ages sixty- nine to seventy. For women, the retirement hazard rate is underpredicted by the OV model at higher ages, particularly at ages seventy- one and seventy- four (figure 12.8B). These results indicate that the model does not seem to predict well the spike in the hazard rate at certain ages, as seen in the actual data.

Figures 12.8C and 12.8D illustrate the predicted versus actual survival

Fig. 12.8A Actual versus predicted retirement rate (men ages fifty to seventy- four)

Fig. 12.8B Actual versus predicted retirement rate (women ages fifty to seventy- four)


rates of retirement for men and women, respectively. Figure 12.8C shows that, for men, the actual survival rate is close to 100 percent between ages fifty and fifty- six, declines quickly up to age sixty- six, and then decreases moderately up to age seventy- four. Meanwhile, the predicted survival rate declines steadily between ages fifty and seventy- four. The result that the pre-dicted survival rate decreases monotonically with age is the same for women

Fig. 12.8C Actual versus predicted retirement survival (men ages fifty to seventy-four)

Fig. 12.8D Actual versus predicted retirement survival (women)


(figure 12.8D): the difference in the rate of decline in the actual survival rate by age is not captured by the predicted survival rate.

Figure 12.9 shows the percentage of people for whom the maximum utility from retirement occurs (i.e., OV becomes zero) at a given age, according to the OV model. The figure also presents the actual proportion of individuals who have retired, at each age. We observe that, for most of the individuals in the sample, the maximum utility from retirement is not achieved until age seventy- four. In other words, the OV continues to be positive until the end point of our calculation. This is because, in our OV model, the additional earnings from continued work exceed the decline in the PDV of pension benefits (see figures 12.6C and 12.6D). In contrast, the actual proportion of individuals who have retired evolves more gradually with age. Hence, our model is not necessarily successful in predicting individuals’ actual retire-ment behavior. The small sample size at each age may be one of the reasons for such results.

12.6 Counterfactual Simulation

As a counterfactual simulation analysis, we examine how individual retire-ment behavior would change if there existed only one retirement path— that is, either the normal retirement path or the disability path. More specifically, using the regression results in column (4) of table 12.3A, we calculate retire-ment probabilities and survival rates to retirement under the two counterfac-

Fig. 12.9 Share having reached max OV- inclusive and retired (ages fifty to seventy-four)


tual cases: when the weight placed on the normal retirement path in calcu-lating OV is zero, and when the weight placed on the disability path is zero.

The results of the simulation are presented in figures 12.10A and 12.10B. If the probability of being part of the disability program is zero, then the probability of retirement would decrease over the age range of fifty to around sixty, compared to the case where individuals have no choice but to be on the disability program. Hence, if the possibility of being on the dis-ability program were absent, the survival rate to retirement would be higher than the rate where the disability program were the only path to retirement: for example, at age sixty, the survival rate would be 80.8 percent in the for-mer case, but 70.1 percent in the latter case. This leads to a difference in the simulated average number of work years over the ages of fifty to sixty- nine between the two counterfactual cases: 15.9 years in the case of there being no disability program, and 14.4 years in the case of there being no normal retirement. Thus, the average number of work years from age fifty to sixty- nine would be 9.5 percent higher if every individual were on the normal retirement path, rather than the disability path.

12.7 Conclusion

This study examined the factors that affect the retirement decisions of the middle- aged and elderly in Japan, focusing especially on their earnings, public pension benefits, and health status. Using two- year panel data from the JSTAR and applying the OV model proposed by Stock and Wise (1990a, 1990b), we found that the probability of retirement has a negative and sig-nificant correlation with the OV, and that the correlation does not depend on the health status. Our counterfactual simulation based on the OV model showed that, if the probability of being enrolled in the disability program were zero, the average years of work when individuals are in their fifties and sixties would increase. However, it should be emphasized that, in Japan— where being enrolled in the disability program is unlikely to make one a can-didate for the retirement path— the result of this simulation does not indi-cate that the labor supplied by the middle- aged and elderly will increase by making the eligibility criteria for disability pension receipts more stringent.

We recognize that there remains much to be addressed in future research. First, we should further elaborate the specifications of the OV model. The value of an OV depends on the parameters of the utility function, such as parameters for converting income to utility and the discount rate; these parameters are tentatively assumed in the current study. Second, we should more precisely project wage profiles and capture different pathways to retire-ment on the basis of further information obtained from official statistical sources. Third, we should also model couples, rather than individuals, as retirement decisions are likely to be made jointly by elderly couples: we should therefore incorporate information about spouses’ and survivors’ pen-sion benefits, which are ignored in this study.

Fig. 12.10A Retirement probabilities by pathway, disability, and normal retirement (ages fifty to sixty- nine)

Fig. 12.10B Survival probabilities by pathway, disability, and normal retirement (ages fifty to seventy)


Appendix

An Overview of Social Security Programs in Japan

Japan’s public old- age pension system consists of three program types: Na-tional Pension Insurance ([NPI]; Kokumin Nenkin), Employees’ Pension Insurance ([EPI]; Kosei Nenkin), and Mutual Aid Insurance ([MAI]; Kyosai Nenkin). It has been mandatory since 1961 for every Japanese national to participate in one of these public pension programs, and every citizen in Japan is eligible for one of them.15

The NPI covers self- employed workers, or forestry or fishery coopera-tive employees; those covered by NPI constitute slightly less than half of all pensioners in Japan. The NPI benefits are disbursed on a flat- rate basis, depending on the number of contribution years (minimum of twenty- five years and maximum of forty years). In 2000, the normal eligibility age for NPI was set at age sixty for both genders; every three years, this age has been scheduled to increase by one year to sixty- five. This reform has been in effect since 2001 for males and 2006 for females.16 As a result of this reform, in 2007— the benchmark year of JSTAR— the normal eligibility age was sixty- three for males and sixty- one for females.

The EPI covers employees in the private sector, and the individuals it cov-ers constitute slightly less than half of all pensioners. Unlike that of NPI, the EPI benefit structure consists of two tiers: a flat- rate component and a wage- proportional component. The calculation of the flat- rate benefit (i.e., the basic pension benefit) is identical to that of NPI. The wage- proportional ben-efit is calculated by considering the career average monthly wage ([CAMW]; Hyojyun Hoshu Getsugaku) and the number of months of premium contribu-tions, as well as a gender and birthday- dependent benefit multiplier. The nor-mal eligibility age for the wage- proportional component has been set at age sixty, but as of 2013 (2018)that age is scheduled to increase by one year every three years, reaching age sixty- five in 2025 for males and 2030 for females.

The MAI covers employees of the public sector and of private schools; those covered by MAI constitute the small portion of pensioners covered by neither NPI nor EPI. The contribution–benefit structure and the normal retirement age for MAI benefits resemble those for EPI benefits in most respects; thus, in the analysis below, we combine EPI and MAI pensioners.

In addition to the core programs, there are three additional features rel-evant to setting up Japan’s retirement pathways: the social security earn-ings test, early/late claiming, and the disability pension program. Most of the previous studies implicitly assume that the age at which one starts to

15. The studies of Oshio, Shimizutani, and Oishi (2010) and Oshio, Oishi, and Shimizutani (2011) describe in detail Japan’s old- age pension program.

16. Japan’s social security program has undergone several large reforms over the last forty years. The studies of Oshio, Shimizutani, and Oishi (2010) and Oshio, Oishi, and Shimizutani (2011) provide detailed descriptions of past reforms.


claim pension benefits corresponds to the retirement age (i.e., marked by one’s departure from the labor force). However, this is not the case in Japan, and such an assumption ignores some important aspects of the association between pension benefits and labor force participation among the elderly.

First, the social security earnings test (Zaishoku Rorei Nenkin) can result in a suspension of payment of part or all of one’s pension benefits if one’s labor income exceeds a certain threshold; the discouraging effect of this test on labor supply has been studied intensively in Japan and in other countries. Among recent studies in Japan, Shimizutani (2013) reveals the discourag-ing effect of the earnings test on the labor supply decisions of workers age sixty to sixty- four years. Shimizutani and Oshio (2013) show that the repeal in 1985 of the earnings test for workers age sixty- five to sixty- nine did not affect the earnings distribution of the elderly, but that its reinstatement in 2002 partially altered earnings distribution.

Under the current program, the earnings test focuses on the average monthly wage and bonus income.17 For workers ages sixty to sixty- four, pension benefit payments are not suspended if the average wage and bonus income per month is less than JPY 280,000; however, benefits are suspended by JPY 0.5 per JPY 1 increase in labor income (i.e., a marginal tax rate of 50 percent) between JPY 280,000 and JPY 460,000, and suspended by JPY 1 per JPY 1 increase in labor income (i.e., a marginal tax rate of 100 percent) in excess of JPY 460,000. For workers age sixty- five and older, the pension benefit payment is not suspended if the average wage and bonus income per month is less than JPY 460,000, but it is suspended by JPY 0.5 per JPY 1 increase in labor income (i.e., a marginal tax rate of 50 percent) in excess of JPY 460,000. Note that the earnings test is applicable only to the second- tier (i.e., wage- proportional) benefit for EPI beneficiaries, and not at all to NPI or MAI beneficiaries.

Second, all three social security programs allow their beneficiaries to claim within a “window” period; indeed, a nontrivial proportion of those benefi-ciaries claim at ages other than the normal eligibility ages. First, NPI allows a ten- year window in claiming benefits, and an individual undergoes ben-efit reductions if he or she claims early, at ages sixty to sixty- four (Kuriage Jyukyu); alternatively, he or she receives a benefit reward if he or she claims late, at ages sixty- six to seventy (Kurisage Jyukyu). The actuarial adjustment rate differs across birth cohorts; for example, for those individuals born after April 2, 1941, the actuarial reduction rate before age sixty- five is 0.5 percent per month, and the actuarial credit rate after age sixty- five is 0.7 percent per month (Shimizutani and Oshio 2012). Second, EPI also allows some flexibility in terms of claim timing, and it differs between flat- rate and wage- proportional benefits. As of 2011, one cannot claim the special benefit (i.e., corresponding to the wage- proportional benefit prior to age sixty- five) earlier or later than the normal eligibility age of sixty years, regardless of gender;

17. The earnings test has been revised many times. Shimizutani (2013) and Shimizutani and Oshio (2013) review previous reforms vis- à- vis the earnings test, over long- term periods.


however, in 2007— when the normal eligibility ages for men and women were sixty- three and sixty- one, respectively— one could claim the flat- rate compo-nent earlier, at ages sixty to sixty- two for males and sixty for females.18 More-over, an EPI beneficiary can claim either the flat- rate or wage- proportional component later than age sixty- five, and thus enjoy an incremental benefit. Note that once one claims his or her benefits— either before or after the nor-mal eligibility age— one cannot change his or her take- up decision.

Third, the disability pension program, which is not specific to the afore-mentioned old- age pension programs, covers some elderly individuals in Japan. While the participation rate in Japan with regard to the disability pension program remains low, many European countries have expanded their respective DI programs since the 1970s; in some countries, receiving DI bene-fits is a typical feature of early retirement (Wise 2012). Oshio and Shimizutani (2012) argue that this is not the case in Japan, and that the low participation in Japan’s disability pension program can be attributed to the stringency of its eligibility criteria. Under the current program, if one consults with a doctor about the cause of disability for the first time before the age of twenty, or if one is an NPI pensioner, one is entitled to receive the Disability Basic Pension benefit, which is disbursed on the basis of disability severity (Grade 1 or 2) and the number of dependent children. In addition, if one consulted a doctor to identify the cause of the disability when one was an EPI (MAI) pensioner, one is entitled to receive a wage- proportional Disability Employees’ Pen-sion benefit or Disability Mutual Aid Pension benefit (for MAI recipients), the amount of which depends on the disability severity (Grades 1–3) and whether or not one has a spouse (Oshio and Shimizutani 2012).

18. There are two types of early claiming in the EPI program: total early claiming (Zenbu Kuriage) and partial early claiming (Ichibu Kuriage). In the former, one can receive a flat- rate benefit at a reduced rate that is identical to that for an NPI beneficiary, but it is no longer eli-gible for the special benefit. In the latter, one can receive both part of the flat- rate component of the special benefit and part of the flat- rate component of the formal benefit (as well as the wage- proportional component). See Shimizutani and Oshio (2012) for the detailed formula. If the duration of EPI participation is lengthy, the flat- rate benefit of the special benefit and the formal component are almost identical, and partial early claiming is in general more advanta-geous than total early claiming. In the current study, we assume that an EPI beneficiary chooses partial early claiming if he or she claims benefits earlier than the normal eligibility age.

Table 12A.1 Principal component analysis on health indicators (factor loadings of the first principal component health index)

1. Difficulty walking 100 m 0.311 12. Difficulty picking up a dime 0.2482. Difficulty lifting/carrying 0.337 13. Ever experienced heart problems 0.0943. Difficulty pushing/pulling 0.340 14. Hospital stay 0.1094. Difficulty with an activity of daily living 0.242 15. Doctor visit 0.0825. Difficulty climbing a few steps 0.315 16. Ever experienced psychological problems 0.0176. Difficulty stooping/kneeling/crouching 0.309 17. Ever experienced a stroke 0.1267. Difficulty getting up from a chair 0.304 18. Ever experienced high blood pressure 0.0758. Self- reported health fair/poor 0.211 19. Ever experienced lung disease 0.0409. Difficulty reaching/extending arm up 0.269 20. Ever experienced diabetes 0.07110. Ever experienced arthritis 0.122 21. Body mass index 0.02611. Difficulty sitting two hours 0.277 22. Ever experienced cancer 0.035

Table 12A.2 Summary statistics of the variables

All Male Female

Continuous variableOV (in ten thousand euro) M 3.22 3.59 2.58

S.D. 2.06 2.16 1.70Health index M. 56.89 56.45 57.65

D.S. 26.57 26.09 27.38Monthly wage (in ten thousand yen) M. 25.41 31.22 15.23

S.D. 18.15 18.4 12.28Enrolled years M. 36.54 39.62 31.24

S.D. 13.36 11.91 14.04Assets (in million yen) M. 11.37 13.44 7.99

S.D. 68.07 85.48 15.45

Binary variablesAge

Less than 55 0.21 0.20 0.2355–59 0.31 0.31 0.3160–64 0.22 0.21 0.2365–69 0.17 0.18 0.1470– 0.10 0.11 0.08

EducationLess than high school 0.29 0.31 0.25High school 0.44 0.40 0.50Two years college/vocational school 0.12 0.08 0.20Four years college or more 0.15 0.21 0.06

Married 0.84 0.90 0.75Spouse working 0.57 0.55 0.60Occupation

Specialist 0.10 0.10 0.09Managers 0.08 0.11 0.02Clerk 0.16 0.12 0.24Salesperson 0.13 0.13 0.13Service 0.14 0.07 0.26Guards 0.01 0.02 0.00Farmers 0.05 0.05 0.05Trans and com. 0.05 0.08 0.01Construction 0.25 0.29 0.17Unknown 0.03 0.03 0.04

Retired in 2009 0.10 0.09 0.11Public pension enrollee:

EPI/MAI enrollee 0.60 0.67 0.50NPI enrollee 0.40 0.33 0.50

1,575 996 579

Note: M = mean; S.D. = standard deviation.


References

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Poterba, James, Steven Venti, and David Wise. 2010. “Family Status Transitions, Latent Health, and the Post- Retirement Evolution of Assets.” NBER Working Paper no. 15789, Cambridge, MA.

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social security programs and retirement around the world ...more recently, oshio, oishi, and...

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